A Microsimulation Platform of Firm Evolution Processes
Bibliographic record
Abstract
Firmography is a fundamental component of urban systems that has not received much research attention. Firmographic events of entry, exit, growth, and relocation affect economic growth, labour dynamics, and result in a complex system of goods movements. This dissertation develops a firm modelling framework, called the firmographic engine. The goal of the framework is to evaluate the implications of policy on firm evolution over time by simulating and forecasting firm behaviour. The engine is composed of firm generation, market introduction, performance evaluation, and firm evolution modules. Firm generation simulates entrepreneurial decisions of firm entry. Market introduction involves the specification and completion of operational strategies. Performance evaluation assesses the outcomes of the operational strategies. Firm evolution simulates growth/shrinkage and exit decisions depending on performance evaluation results. Three firmographic events are studied in detail: firm start-up size, growth, and survival. Ordered logit models are estimated for firm start-up size in terms of the firmâ s number of employees and tangible assets. Autoregressive Distributed-Lag models (ARDL) are estimated to represent firm growth. Parametric and non-parametric analyses for firm survival are presented. The non-parametric analysis introduces survival and hazard rates characterized by industry class, province, firm age, and firm size. The parametric analysis includes a discrete-time hazard duration model of firm failure. The results show that firm start-up size, growth and survival are influenced by economic growth, industry dynamics, competition, and firm location and characteristics. Models of freight outsourcing decisions by Canadian manufacturers are also presented. Binary logit and multinomial logit models are estimated. The models show that freight outsourcing is driven by firm strategies, supplier locations, government incentives, innovation and technology, industry dynamics, economic conditions, and competition. Models of international versus local outsourcing are also explored. Statistics Canadaâ s T2-LEAP and Survey of Innovation and Business Strategy (SIBS) databases are utilized for this research. The longitudinal T2-LEAP dataset is used to estimate models of firm start-up size, firm growth, and firm failure of Canadian firms for the period of 2001 to 2012. The cross-sectional SIBS dataset, for the years of 2009 and 2012, is used to estimate models of freight outsourcing by Canadian manufacturers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".